Abstract
This paper discusses the approach that is being taken for parallelising the WAQUA/Kalman software of the Dutch Institute for Coastal and Marine Management. This software is used for numerical simulation of flow and transport phenomena in coastal waters and incorporates a Kalman filtering procedure to assimilate observational data into the simulation. In particular the Kalman filtering part of the software is very time-consuming which prohibits its use in e.g. operational storm surge prediction. Parallelisation is being considered to reduce this run-time.
Instead of parallelising the code as it is, it is first decomposed into a number of components which are coupled through an advanced message passing library. After this, each of the components can be parallelised in the way that is most appropriate for the computations in the component. If the original code were parallelised directly, it would not be possible to accommodate the various forms of parallelism that are available in the program. The decomposition into components itself already gives a clear benefit because it enhances the maintainability and reusability of the code.
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References
R. E. Kalman. A new approach to linear filter and prediction theory. J. Basic Engr., 82D:35–45, 1960.
R.E. Kalman and R.S. Bucy. New results in linear filtering and prediction theory. J. Basic. Engr, 83D:95–108, 1961.
P.M. Lyster, S.E. Cohn, R. Ménard, L.-P. Chang, S.-J. Lin, and R.G. Olsen. Parallel implementation of a kalman filter for constituent data assimilation. Technical Report DAO Office Note 97-02, Data Assimilation Office, Goddard Laboratory for Atmospheres, NASA, April 1997.
M. Morf, J.R. Dobbins, B. Friedlander, and T. Kailath. Square-root algorithms for parallel processing in optimal estimation. Automatica, 15:299–306, 1979.
M.E. Phillipart et al. Datum2: Data assimilation with altimetry techniques used in a tidal model. Technical Report NRSP-2 98-19, BCRS, 1998.
M.R.T. Roest. Partitioning for Parallel Finite Difference Computations in Coastal Water Simulation. PhD thesis, Delft University of Technology, 1997.
M. Verlaan. Efficient Kalman Filtering Algorithms for Hydrodynamic Models. PhD thesis, Delft University of Technology, April 1998.
M. Verlaan and A. W. Heemink. Reduced rank square root filters for large scale data assimilation problems. In Second Int'l Symp on Assimilation of Observations in Meteorology and Oceanography, pages 247–252. World Meteorological Organisation, march 1995.
M. Verlaan and A. W. Heemink. Tidal flow forecasting using reduced rank square root filters. Stochastic Hydrology and Hydraulics, 11:349–368, 1997.
E.A.H. Vollebregt. Abstract level parallelization of finite difference methods. Scientific Programming, 6:331–344, 1997.
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© 1999 Springer-Verlag
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Roest, M.R.T., Vollebregt, E.A.H. (1999). Decomposition of complex numerical software into cooperating components. In: Sloot, P., Bubak, M., Hoekstra, A., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1999. Lecture Notes in Computer Science, vol 1593. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100664
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DOI: https://doi.org/10.1007/BFb0100664
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